news_ne = news_ne.drop('bert_topics_words', axis = 1)
# Assuming 'Representative_Docs' contains lists of strings for topic in range(1, 11): doc_list = negative_topic_df[negative_topic_df['Topic'] == topic]['Representative_Docs'].iloc[0] if isinstance(doc_list, list): doc_str = ' '.join(doc_list) # Join list into a single string else: doc_str = doc_list # If it's already a string # Generate word cloud wordcloud_doc = WordCloud(background_color='white').generate(doc_str) # Plotting plt.figure(figsize=(10, 5)) plt.imshow(wordcloud_doc, interpolation='bilinear') plt.title(f"Word Cloud for Topic {topic}") plt.axis('off') plt.show()